The present disclosure relates generally to vision-based lane sensing, and more particularly, to utilizing map software to assist in vision-based lane sensing.
Vision-based lane sensing (LS) systems detect roadway lane markings and can utilize this information for lane departure warning (LDW), road departure warning and lane keeping (LK) purposes in addition to other purposes (e.g., road geometry prediction). In general, the LS algorithms utilize information from both right and left lane markings to inform the driver of an inadvertent lane deviation, or to steer or keep the vehicle within the lane using, for example, electric power steering (EPS) or active front steering (AFS).
In most situations, the LS system utilizes the contrast between the lane marking and the pavement to detect the markings. For example, a bright white lane marking on black tar pavement can be detected by the image processor without too much difficulty. As this contrast deteriorates, so does the lane sensing performance. In this regard, it is more difficult for the image processor to detect yellow lane markings in a gray scaled image (and to a lesser degree color image) because of the lower intensities that they generate. At the same time, there is an abundance of yellow lane markings on roadways in the United States. For example, as shown in FIG. 1, freeways have yellow lane markings on the left side of the road. Another example, as shown in FIG. 2, is that two-way roads have yellow lane markings dividing the road. It would be very helpful to the vision system if it could be cued to the presence of such yellow colored markers so as to allow the vision system to better adjust its filters to recognize such lane markers. This would also result in reduced computational effort because the vision system would use algorithms directed to detecting yellow lane markings.